The Foreign Investor Bias and Its Linguistic Origins
Bibliographic record
Abstract
We study how misaligned language between the investor and the firm contributes to the underweighting of foreign securities in an international portfolio. In particular, we document a significant U.S. institutional investor bias against firms located in Quebec relative to firms located in the rest of Canada (ROC). The differential bias is surprising given that (i) Quebec and the other Canadian provinces share the same nationality, federal law, stock exchange, and accounting standards; (ii) their regulatory filings are prepared in English and French; and (iii) U.S. institutional investors are sophisticated and located close to Quebec and the ROC. We also examine Quebec firms with different levels of French versus English online presences as well as those with CEOs who have U.S. work experience or board members or financial analysts who reside in the United States. We find that each factor affects the relative underweighting of investment in Quebec versus the ROC. Finally, we contrast the holdings of institutional investors located in the United Kingdom and France to bolster our conclusion that incongruent languages contribute to the underweighting of Quebec firms relative to firms in the ROC. This paper was accepted by Suraj Srinivasan, accounting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".